---
title: "Hands-On-Large-Language-Models vs Awesome-AIGC-Tutorials"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-luban-agi-awesome-aigc-tutorials"
tools: ["handsonllm-hands-on-large-language-models", "luban-agi-awesome-aigc-tutorials"]
---

# Hands-On-Large-Language-Models vs Awesome-AIGC-Tutorials

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick Hands-On-Large-Language-Models if hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵; pick Awesome-AIGC-Tutorials if awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry.

[Hands-On-Large-Language-Models](https://www.llm-book.com/) reports 29k GitHub stars, 6.7k forks, and 40 open issues, last pushed Apr 24, 2026. [Awesome-AIGC-Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials) has 4.5k stars, 298 forks, and 10 open issues, last pushed Mar 31, 2024. Figures are from public GitHub metadata via [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) and [Awesome-AIGC-Tutorials's repository](https://github.com/luban-agi/Awesome-AIGC-Tutorials).

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) |
| --- | --- | --- |
| Tagline | Code for the book 'Hands-On Large Language Models' | Curated tutorials and resources for Large Language Models, AI Painting, and more |
| Stars | 29,162 | 4,547 |
| Forks | 6,662 | 298 |
| Open issues | 40 | 10 |
| Language | Jupyter Notebook | - |
| Adopt for | Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵 | Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. |
| Categories | Developer Tools, LLM Frameworks | Developer Tools, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 146d | 902d |
| Open issues (now) | 40 | 10 |
| Stars delta | +910 (30d) | +25 (30d) |
| Open issues delta | +2 (30d) | 0 (30d) |
| Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/luban-agi-awesome-aigc-tutorials/trust.md) |

## Decision facts: Hands-On-Large-Language-Models

- **Adopt for:** Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵

## Decision facts: Awesome-AIGC-Tutorials

- **Requirements:** No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.
- **Adopt for:** Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry.
- **License detail:** MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors.

## Choose when

### Choose Hands-On-Large-Language-Models if…

- License: Hands-On-Large-Language-Models is Apache-2.0, Awesome-AIGC-Tutorials is MIT.
- Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, large-language-models, llms.
- Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.

### Choose Awesome-AIGC-Tutorials if…

- License: Awesome-AIGC-Tutorials is MIT, Hands-On-Large-Language-Models is Apache-2.0.
- Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial..
- Tags unique to Awesome-AIGC-Tutorials: ai, aigc, chatgpt, deep-learning.
- Also covers Model Training.
- If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources.

## When NOT to use Hands-On-Large-Language-Models

- Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning.
- Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM.
- If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.

## When NOT to use Awesome-AIGC-Tutorials

- Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples.
- Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis.

## Common questions

### What is the difference between Hands-On-Large-Language-Models and Awesome-AIGC-Tutorials?

Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. Awesome-AIGC-Tutorials: Curated tutorials and resources for Large Language Models, AI Painting, and more. See the comparison table for live GitHub stats and shared categories.

### When should I choose Hands-On-Large-Language-Models over Awesome-AIGC-Tutorials?

Choose Hands-On-Large-Language-Models over Awesome-AIGC-Tutorials when License: Hands-On-Large-Language-Models is Apache-2.0, Awesome-AIGC-Tutorials is MIT; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, large-language-models, llms; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.

### When should I choose Awesome-AIGC-Tutorials over Hands-On-Large-Language-Models?

Choose Awesome-AIGC-Tutorials over Hands-On-Large-Language-Models when License: Awesome-AIGC-Tutorials is MIT, Hands-On-Large-Language-Models is Apache-2.0; Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.; Tags unique to Awesome-AIGC-Tutorials: ai, aigc, chatgpt, deep-learning; Also covers Model Training; If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources.

### When should I avoid Hands-On-Large-Language-Models?

Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.

### When should I avoid Awesome-AIGC-Tutorials?

Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis.

### Is Hands-On-Large-Language-Models or Awesome-AIGC-Tutorials more popular on GitHub?

Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 4,547). Stars measure visibility, not whether either tool fits your constraints.

### Are Hands-On-Large-Language-Models and Awesome-AIGC-Tutorials open source?

Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, Awesome-AIGC-Tutorials: MIT).

### Where can I find alternatives to Hands-On-Large-Language-Models or Awesome-AIGC-Tutorials?

GraphCanon lists graph-backed alternatives at [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) and [Awesome-AIGC-Tutorials alternatives](/tools/luban-agi-awesome-aigc-tutorials/alternatives) ([Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-large-language-models/alternatives.md), [Awesome-AIGC-Tutorials markdown twin](/tools/luban-agi-awesome-aigc-tutorials/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/handsonllm-hands-on-large-language-models-vs-luban-agi-awesome-aigc-tutorials.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Hands-On-Large-Language-Models or Awesome-AIGC-Tutorials?

Hands-On-Large-Language-Models: Slowing. Awesome-AIGC-Tutorials: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for Hands-On-Large-Language-Models and Awesome-AIGC-Tutorials?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/trust); [Awesome-AIGC-Tutorials trust report](/tools/luban-agi-awesome-aigc-tutorials/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models`](/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_